Objective: The aim of the research is to make a bibliometric analysis of articles published using machine learning-sports key concepts. For this purpose, 654 studies published in the sources scanned in the Web of Science Core Collection database between 1999-2021 will be examined bibliometrically and the trend in the last 23 years will be revealed. Material and Methods: The database was searched using the keywords 'machine learning' and 'sports' and the number of studies for years, the average number of citations per year, the journals and authors that published the most on this subject, the citation burst values of the authors, the countries and cooperation status of the responsible authors, the most the cited articles, word cloud and word tree map and conceptual structures were examined under their sub-titles. Results: According to the results obtained, it can be said that the interest in the subject has increased after 2014. The journal in which the articles on this subject were published the most was 'Sensors', and it was determined that Musa RM was the author who wrote the most articles. The most cited work was written by Li and Wu in 2010. Conclusion: In the articles written, it has been determined that Australia and the United Kingdom are the countries most open to cooperation, and the most used concepts in the keyword and title section are 'performance' and 'learning'. It is believed that the results obtained will shed light on researchers who want to conduct research on this subject.
Keywords: Machine learning; sports; bibliometric analysis; network analysis
Amaç: Bu çalışmanın amacı, 'machine learning-sports' anahtar kavramları kullanılarak yayımlanan makalelerin bibliyometrik analizini yapmaktır. Bu amaç doğrultusunda 1999-2021 yılları arasında Web of Science Core Collection veri tabanında taranan kaynaklarda yayımlanan 654 çalışma bibliyometrik açıdan incelenerek son 23 yıldaki eğilim ortaya koyulacaktır. Gereç ve Yöntemler: Veri tabanında 'machine learning' ve 'sports' anahtar kavramı kullanılarak tarama gerçekleştirilmiş ve yıllara ilişkin çalışma sayısı, yıllık ortalama alıntı sayısı, bu konuda en çok yayın yapan dergiler ve yazarlar, yazarların atıf patlama değerleri, sorumlu yazarların ülkeleri ve iş birliği durumları, en çok atıf alan makaleler, kelime bulutu ve kelime ağacı yapıları ve kavramsal yapıları alt başlıklarında incelenmiştir. Bulgular: Elde edilen sonuçlara göre konuya olan ilginin 2014 yılından sonra arttığı söylenebilir. Bu konudaki makalelerin en fazla yayımlandığı dergi 'Sensors' olup, Musa RM'nin en fazla makale yazan yazar olduğu belirlenmiştir. En fazla atıfı alan çalışma 2010 yılında Li ve Wu tarafından yazılmıştır. Sonuç: Yazılan makalelerde Avustralya ve Birleşik Krallık'ın iş birliğine en açık ülkeler olduğu, anahtar kelime ve başlık kısmında en çok kullanılan kavramların 'performance' ve 'learning' olduğu tespit edilmiştir. Elde edilen sonuçların bu konuda araştırma yapmak isteyen araştırmacılara ışık tutacağına inanılmaktadır.
Anahtar Kelimeler: Makine öğrenmesi; spor; bibliometrik analiz; ağ analizi
- Pandya R, Nadiadwala S, Shah R, Shah M. Buildout of methodology for meticulous diagnosis of K-complex in EEG for aiding the detection of Alzheimer's by artificial intelligence. Augmented Human Research. 2020;5(1):1-8. [Crossref]
- Parekh V, Shah D, Shah M. Fatigue detection using artificial intelligence framework. Augmented Human Research. 2020;5(1):1-17. [Crossref]
- Patel D, Shah D, Shah M. The intertwine of brain and body: a quantitative analysis on how big data influences the system of sports. Annals of Data Science. 2020;7(1):1-16. [Crossref]
- Ahir K, Govani K, Gajera R, Shah M. Application on virtual reality for enhanced education learning, military training and sports. Augmented Human Research. 2020;5(1):1-9. [Crossref]
- Analytic Sinsight [Internet]. © 2021 Analytics Insight [Cited: April 11, 2021]. Data science and artificial intelligence is revolutionizing the sports industry. Available from: [Link]
- Baboota R, Kaur H. Predictive analysis and modelling football results using machine learning approach for English Premier League, International Journal of Forecasting. 2019;35(2):741-55. [Crossref]
- Ievoli R, Palazzo L, Ragozini, G. On the use of passing network indicators to predict football outcomes. Knowledge-Based Systems. 2021;222:106997. [Crossref]
- Bartlett R. Artificial intelligence in sports biomechanics: new dawn or false hope? J Sports Sci Med. 2006;5(4):474-9. [PubMed] [PMC]
- Dai X, Li S. Application analysis of wearable technology and equipment based on artificial intelligence in volleyball. Mathematical Problems in Engineering. 2021;5572389:10. [Crossref]
- Ćwiklinski B, Giełczyk A, Choraś M. Who will score? A machine learning approach to supporting football team building and transfers. Entropy (Basel). 2021;23(1):90. [Crossref] [PubMed] [PMC]
- Analyticssteps [Internet]. ©Analytics Steps Infomedia LLP 2020-2022 [Cited: April 9, 2021]. How Artificial Intelligence Plays Football? Available from: [Link]
- Medium [Internet]. [Cited: April 10, 2021]. Current AI/machine learning trends in football. Available from: [Link]
- ThinkML [Internet]. © 2021 ThinkML [Cited: April 13, 2021]. Artificial intelligence (AI) in football. Available from: [Link]
- Sport Performance Analysis [Internet]. © 2018 Sport Performance Analysis [Cited: April 10, 2021]. Artificial Intelligence (AI) in Sports. Available from: [Link]
- Fusioninformatics [Internet]. © 2019 Fusion Informatics Copyright [Cited: April 13, 2021]. Impact of Artificial Intelligence in the Sports Industry. Available from: [Link]
- LawInSport [Internet]. © 2010 - 2022 LawInSport Limited [Cited: April 12, 2021]. Artificial intelligence in sports-the legal and ethical issues at play. Available from: [Link]
- Al U, Soydal İ, Yalçın H. Bibliyometrik özellikleri açısından Bilig'in değerlendirilmesi [An evaluation of the bibliometric features of bilig]. Bilig, Güz. 2010;(55):1-20. [Link]
- Huang YL, Ho YS, Chuang KY. Bibliometric analysis of nursing research in Taiwan 1991-2004. J Nurs Res. 2006;14(1):75-81. [Crossref] [PubMed]
- Al U, Tonta Y. Atıf analizi: Hacettepe Üniversitesi Kütüphanecilik Bölümü tezlerinde atıf yapılan kaynaklar [Citation analysis: sources cited in dissertations completed at Hacettepe University Department of Librarianship]. Bilgi Dünyası. 2004;5(1):19-47. [Crossref]
- Osareh F. Bibliometrics, citation analysis and co-citation analysis: A review of literature I. Libri. 1996;46(3):149-58. [Crossref]
- Zan BU. Türkiye'de bilim dallarında karşılaştırmalı bibliyometrik analiz çalışması [Doktora tezi]. Ankara: Ankara Üniversitesi; 2012. [Link]
- McBurney MK, Novak PL. What is bibliometrics and why should you care. In Proceedings. IEEE International Professional Communication Conference. 2002. p.108-14. [Link]
- Borgman CL, Furner J. Scholarly communication and bibliometrics. Annual Review of Information Science and Technology. 2002;36(1):2-72. [Crossref]
- Bilgiç M, Işın A. Embarking on a journey: a bibliometric analysis of the relative age effect in sport science. German Journal of Exercise and Sport Research. 2022;1-8. [Crossref]
- Doğru M, Güzeller CO, Çelik M. Geçmişten günümüze sürdürülebilir kalkınma ve eğitim alanında: bibliyometrik bir analiz [A bibliometric analysis in the field of sustainable development and education from past to present]. Adıyaman University Journal of Educational Sciences. 2019;9(1):42-68. [Crossref]
- Jiménez-García M, Ruiz-Chico J, Pe-a-Sánchez AR, López-Sánchez JA. A bibliometric analysis of sports tourism and sustainability (2002-2019). Sustainability. 2020;12(7):2840. [Crossref]
- Kurtuluş MA, Bilen K. A bibliometric analysis on nature of science: a review of the research between 1986-2019. Scientific Educational Studies. 2021;5(1):47-65. [Link]
- Kurtuluş MA, Tatar N. An analysis of scientific articles on science misconceptions: a bibliometric research. Ilkogretim Online. 2021;20(1):192-207. [Crossref]
- Aria M, Cuccurullo C. Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics. 2017;11(4):959-75. [Crossref]
- Li N, Wu DD. Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems. 2010;48(2):354-68. [Crossref]
- Barshan B, Yüksek MC. Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. The Computer Journal. 2014;57(11):1649-67. [Crossref]
- Zhang S, Rowlands AV, Murray P, Hurst TL. Physical activity classification using the GENEA wrist-worn accelerometer. Med Sci Sports Exerc. 2012;44(4):742-8. [Crossref] [PubMed]
- Wang CH, Liu JF, Hong TP, Tseng SS. A fuzzy inductive learning strategy for modular rules. Fuzzy Sets and Systems. 1999;103(1):91-105. [Crossref]
- Zhang Z, He T, Zhu M, Sun Z, Shi Q, Zhu J, et al. Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications. npj Flexible Electronics. 2020;4(1):1-12. [Crossref]
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